As of 2019:
~ 40% of cases were being tested with Xpert, compared to ~ 3% in 2014 and 23.5% in 2015, on average
Mato Grosso, Pará, and Pernambuco had the lowest average testing coverage
Roraima, Amazonas, and Rondônia had the highest average testing coverage
State-level hierarchical generalized additive model (GAM) that models the prevalence of RR-TB positive cases per quarter among incident TB cases between 2014-2019
Fit smoothing functions to reduce the noise we were seeing in previous models
Models risk of positivity by characteristics of patient and municipality where they reside
Separate models for new TB cases, re-entry cases, and relapsed cases
result ~ s(state, bs = "re") + s(time) + s(time, by = state, id = 1) + age_cat +
hiv_status + sex + health_unit + bf_cat + urban_cat + has_prison
Random intercept for each state (patient state of residence)
A different smooth function for time by state with a shared smoothing parameter
Each state-level smoothing parameter varies around a grand smooth function for time to allow for pooling across states
Fixed effects for patient-level characteristics:
Fixed effects for municipality-level characteristics:
Model 1 - Adjusted:
Model 2 - Adjusted; Restricted > 2015
Run separately by case type (e.g. new, relapse, re-entry) and for all cases
Model A - Adjusted:
Model B - Model A + interaction term (HIV, sex, age)
Model C - Model A, restricted to >2015
Time trends in RR-TB positivity (Model A - Adjusted):
Note: The following figures show the same model output, only the Y axis changes to show variation within each state.
Time trends in RR-TB positivity (Model A - Adjusted):
Note: The following figures show the same model output, only the Y axis changes to show variation within each state.
Time trends in RR-TB positivity (Model A - Adjusted):
Time trends in RR-TB positivity (Model A - Adjusted):